Metadata-Version: 2.1
Name: MACS3
Version: 3.0.0a2
Summary: Model Based Analysis for ChIP-Seq data
Home-page: http://github.com/taoliu/MACS/
Author: Tao Liu
Author-email: vladimir.liu@gmail.com
License: UNKNOWN
Description: # MACS: Model-based Analysis for ChIP-Seq
        
        ![Status](https://img.shields.io/pypi/status/macs3.svg) ![License](https://img.shields.io/github/license/macs3-project/MACS) ![Programming languages](https://img.shields.io/github/languages/top/macs3-project/MACS) ![CI x64](https://github.com/macs3-project/MACS/workflows/CI%20x64/badge.svg) ![CI non x64](https://github.com/macs3-project/MACS/workflows/CI%20non%20x64,%20python%203.7/badge.svg)
        
        [![PyPI download](https://img.shields.io/pypi/dm/macs3?label=pypi%20downloads)](https://pypistats.org/packages/macs3) [![Bioconda download](https://img.shields.io/conda/dn/bioconda/macs3?label=bioconda%20downloads)](https://anaconda.org/bioconda/macs3)
        
        Latest Release:
        * Github: [![Github Release](https://img.shields.io/github/v/release/macs3-project/MACS)](https://github.com/macs3-project/MACS/releases)
        * PyPI: [![PyPI Release](https://img.shields.io/pypi/v/macs3.svg) ![PyPI Python Version](https://img.shields.io/pypi/pyversions/MACS3) ![PyPI Format](https://img.shields.io/pypi/format/macs3)](https://pypi.org/project/macs3/)
        * Bioconda: [![Bioconda Release](https://img.shields.io/conda/v/bioconda/macs3) ![Bioconda Platform](https://img.shields.io/conda/pn/bioconda/macs3)](https://anaconda.org/bioconda/macs3)
        * Debian Med: [![Debian Stable](https://img.shields.io/debian/v/macs/stable?label=debian%20stable)](https://packages.debian.org/stable/macs) [![Debian Unstable](https://img.shields.io/debian/v/macs/sid?label=debian%20sid)](https://packages.debian.org/sid/macs)
        
        ## Introduction
        
        With the improvement of sequencing techniques, chromatin
        immunoprecipitation followed by high throughput sequencing (ChIP-Seq)
        is getting popular to study genome-wide protein-DNA interactions. To
        address the lack of powerful ChIP-Seq analysis method, we presented
        the **M**odel-based **A**nalysis of **C**hIP-**S**eq (MACS), for
        identifying transcript factor binding sites. MACS captures the
        influence of genome complexity to evaluate the significance of
        enriched ChIP regions and MACS improves the spatial resolution of
        binding sites through combining the information of both sequencing tag
        position and orientation. MACS can be easily used for ChIP-Seq data
        alone, or with a control sample with the increase of
        specificity. Moreover, as a general peak-caller, MACS can also be
        applied to any "DNA enrichment assays" if the question to be asked is
        simply: *where we can find significant reads coverage than the random
        background*.
        
        **Please note that current MACS3 is still in alpha stage. However, we
        utilize Github Action to implement the CI (Continous Integration) to
        make sure that the main branch passes unit testing on certain
        functions and subcommands to reproduce the correct outputs. We will
        add more new features in the future.**
        
        ## Recent Changes for MACS (3.0.0a2)
        
        ### 3.0.0a2
        	* New features
        	
        	1) Speed/memory optimization.  Use the cykhash to replace python
            dictionary. Use buffer (10MB) to read and parse input file (not
            available for BAM file parser). And many optimization tweaks.
        
        	2) Code cleanup. Reorganize source codes.
        
        	3) Unit testing.
        
        	4) R wrappers for MACS -- MACSr
        
            5) Switch to Github Action for CI, support multi-arch testing
            including x64, armv7, aarch64, s390x and ppc64le.
        
            6) MACS tag-shifting model has been refined. Now it will use a
            naive peak calling approach to find ALL possible paired peaks at +
            and - strand, then use all of them to calculate the
            cross-correlation.
        
            7) Call variants in peak regions directly from BAM files. The
        	function was originally developed under code name SAPPER. Now
        	SAPPER has been merged into MACS. Also, `simde` has been added as
        	a submodule in order to support fermi-lite library under non-x64
        	architectures.
        
        ## Install
        
        The common way to install MACS is through
        [PYPI](https://pypi.org/project/macs3/)) or
        [conda](https://anaconda.org/bioconda/macs3). Please check the
        [INSTALL](./docs/INSTALL.md) document for detail.
        
        ## Usage
        
        Example for regular peak calling on TF ChIP-seq:
        
        `macs3 callpeak -t ChIP.bam -c Control.bam -f BAM -g hs -n test -B -q 0.01`
        
        Example for broad peak calling on Histone Mark ChIP-seq:
        
        `macs3 callpeak -t ChIP.bam -c Control.bam --broad -g hs --broad-cutoff 0.1`
        
        Example for peak calling on ATAC-seq (paired-end mode):
        
        `macs3 callpeak -f BAMPE -t ATAC.bam -g hs -n test -B -q 0.01`
        
        There are currently twelve functions available in MAC3 serving as
        sub-commands. Please click on the link to see the detail description
        of the subcommands.
        
        Subcommand | Description
        -----------|----------
        [`callpeak`](./docs/callpeak.md) | Main MACS3 Function to call peaks from alignment results.
        [`bdgpeakcall`](./docs/bdgpeakcall.md) | Call peaks from bedGraph output.
        [`bdgbroadcall`](./docs/bdgbroadcall.md) | Call broad peaks from bedGraph output.
        [`bdgcmp`](./docs/bdgcmp.md) | Comparing two signal tracks in bedGraph format.
        [`bdgopt`](./docs/bdgopt.md) | Operate the score column of bedGraph file.
        [`cmbreps`](./docs/cmbreps.md) | Combine BEDGraphs of scores from replicates.
        [`bdgdiff`](./docs/bdgdiff.md) | Differential peak detection based on paired four bedGraph files.
        [`filterdup`](./docs/filterdup.md) | Remove duplicate reads, then save in BED/BEDPE format.
        [`predictd`](./docs/predictd.md) | Predict d or fragment size from alignment results.
        [`pileup`](./docs/pileup.md) | Pileup aligned reads (single-end) or fragments (paired-end)
        [`randsample`](./docs/randsample.md) | Randomly choose a number/percentage of total reads.
        [`refinepeak`](./docs/refinepeak.md) | Take raw reads alignment, refine peak summits.
        [`callvar`](./docs/callvar.md) | Call variants in given peak regions from the alignment BAM files.
        
        
        For advanced usage, for example, to run `macs3` in a modular way,
        please read the [advanced usage](./docs/advanced_usage.md). There is a
        [Q&A](./docs/qa.md) document where we collected some common questions
        from users.
        
        ## Contribute
        
        Please read our [CODE OF CONDUCT](./CODE_OF_CONDUCT.md) and
        [How to contribute](./CONTRIBUTING.md) documents.
        
        ## Ackowledgement
        
        MACS3 project is sponsored by
        [CZI EOSS](https://chanzuckerberg.com/eoss/). And we particularly want
        to thank the user community for their supports, feedbacks and
        contributions over the years.
        
        ## Other useful links
        
         * [Cistrome](http://cistrome.org/)
         * [bedTools](http://code.google.com/p/bedtools/)
         * [UCSC toolkits](http://hgdownload.cse.ucsc.edu/admin/exe/)
         * [deepTools](https://github.com/deeptools/deepTools/)
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Cython
Requires-Python: >=3.6
Description-Content-Type: text/markdown
